Systems and methods for generating security improvement plans for entities

ABSTRACT

A computer-implemented method is provided for statistical modeling of entities of a particular type. The method can include obtaining entity data including a plurality of entity data sets, each entity data set associated with a respective entity and including values for one or more static parameters indicative of a type of the entity. Each entity data set can include (i) values for input parameter(s) indicative of a security profile of the entity and (ii) a value of a security class parameter indicative of a security class of the entity based on the values of the input parameters. The method can include training a statistical classifier to infer a value of the security class parameter indicative of the security class of a particular entity of the particular type based on values of one or more of the input parameters indicative of a security profile of the particular entity.

TECHNICAL FIELD

The following disclosure is directed to methods and systems forgenerating a security improvement plan for an entity and, morespecifically, methods and systems for generating a security improvementplan for an entity based on security ratings of similar entities.

BACKGROUND

Ratings enable quantitative comparisons among entities (e.g., companies,students, automobiles, etc.). For example, ratings can be used byconsumers to determine whether to buy from a particular company. Inanother example, ratings can be used by potential employees to determinewhether to work at particular company. Thus, entities subject to aratings scheme typically strive to improve their respective ratings toenhance their standing in their industry or community. One type ofratings scheme pertains to the security of an entity. Specifically, anentity (e.g., a company) can be rated based on past cybersecurity eventsand/or future cybersecurity risks. Aside from the company itself, theremay be multiple stakeholders, e.g., insurance companies, businesspartners, and clients, that are invested in an improved security ratingof the particular company.

Conventional methods utilize brittle rules or to use summary statisticsfrom a vast data set to derive improvement plans. However, these methodscan lead to crude or unrealistic plans for most entities.

SUMMARY

Disclosed herein are systems and methods for generating a securityimprovement plan for an entity with the goal of improving its securityrating. An entity can include an organization, a company, a group, aschool, a government, etc. An entity may be characterized by one or morestatic parameters, e.g., entity size, entity industry, entity location,etc., as these aspects of the entity do not typically change. It isunderstood that some of these aspects may indeed change over time (e.g.,an entity may sell off a part of its business resulting in a decreasedsize, or it may venture into a new industry or location). In someembodiments, if a static parameter of an entity changes, the statisticalclassifier, as discussed below, may be retrained based on the changedvalue of the static parameter.

An improved security rating reflects improvements made to the securityprofile of the entity. Specifically, the security profile of an entitycan be indicated by one or more input parameters, e.g., a number ofbotnet infections of the entity's computer network or a number ofmalware-infected servers associated with the entity. These inputparameters are typically modifiable in that an entity can change orimprove the value of the parameter, thereby improving its securityrating. For example, an entity can strive to decrease the number ofbotnet infections or decrease the number of malware-infected servers. Bydoing so, an entity's security rating may increase, e.g., from 680 to720, indicating an improved ability to withstand or preventcybersecurity attacks. An improved security rating can also increaseconfidence of various stakeholders of the entity that the entity is moresecure and/or protected from cybersecurity risks that it had previouslybeen.

In many instances, for a given entity, the number of input parameterscan be significant and, therefore, the space of possible improvementplans can be quite large. In some embodiments, the respective values oftwo or more input parameters may be correlated with one another. In someembodiments, the respective values of two or more input parameters maybe interdependent. Though these relationships may exist, in manyinstances, the relationships may not be apparent or well-understood.This suggests that some or much of an entity's improvement plan spacemay not be sensible or achievable. In some cases, some of theseimprovement plans are unachievable by the particular entity because theyare dependent on parameters that are difficult for the particular entityto modify. Therefore, determining an achievable improvement plan for anentity can be difficult due to the large space of possible plans and theimplausibility of many areas of the space. Therefore, generating anachievable security improvement plan can depend on reducing the largespace of possible improvement plans and eliminating unachievableportions of improvement plans.

Some embodiments of systems and methods described herein are configuredto generate a feasible security improvement plan for the entity. Afeasible security improvement plan is important to provide the entitywith realistic, achievable goals with a reasonable expectation and/or areasonable likelihood of achieving those goals. A security improvementplan can include value(s) for one or more modifiable input parameters ofthe entity such that the value(s) contribute to an increase in thesecurity rating of the entity. The exemplary systems and methoddescribed herein can focus the space of possible improvement plans byusing data related to similar entities that share the parameters of aparticular entity for which the plan is generated.

In accordance with an embodiment of the disclosure, acomputer-implemented method is provided for statistical modeling ofentities of a particular type. The method can include obtaining entitydata including a plurality of entity data sets, each entity data setassociated with a respective entity and including values for one or morestatic parameters indicative of a type of the entity. The values of thestatic parameters for each of the entity data sets can indicate that thetype of the entity matches the particular type, and each entity data setcan include (i) values for one or more input parameters indicative of asecurity profile of the entity and (ii) a value of a security classparameter indicative of a security class of the entity based on thevalues of the input parameters. The method can include training astatistical classifier to infer a value of the security class parameterindicative of the security class of a particular entity of theparticular type based on values of one or more of the input parametersindicative of a security profile of the particular entity. The trainingthe statistical classifier can include fitting the statisticalclassifier to the plurality of entity data sets.

Various embodiments of the method can include one or more of thefollowing features. The static parameters can include (i) entity size,(ii) entity industry, and/or (iii) entity location. The values of two ormore static parameters for each of the entity data sets can indicatethat the type of the entity matches the particular type. The method caninclude selecting a target value for the security class parameterindicative of the security class for the particular entity. Theplurality of entity data sets can include one or more entity data setsfor which the value of the security class parameter is lower than thetarget value and one or more entity data sets for which the value of thesecurity class parameter is at or above than the target value. Theplurality of entity data sets includes at least three entity data setsfor which the value of the security class parameter is lower than thetarget value and at least three entity data set for which the value ofthe security class parameter is at or above than the target value.

The security profile can include security practices and/or a securityrecord of an entity. One or more input parameters indicative of thesecurity profile of the entity can include: (a) an amount of capitalinvestment in security of the entity; (b) a measure of employee trainingin security of the entity; (c) a measure of organization of a teamdedicated to information security; and/or (d) an amount of budgetdedicated to information security. One or more input parametersindicative of the security profile of the entity can include: (i) anumber and/or severity of botnet infection instances of a computersystem associated with the entity; (ii) a number of spam propagationinstances originating from a computer network associated with theentity; (iii) a number of malware servers associated with the entity;(iv) a number of potentially exploited devices associated with theentity; (v) a number of hosts authorized to send emails on behalf ofeach domain associated with the entity; (vi) a determination of whethera DomainKeys Identified Mail (DKIM) record exists for each domainassociated with the entity and/or a key length of a public keyassociated with a Domain Name System (DNS) record of each domainassociated with the entity; (vii) an evaluation of a Secure SocketsLayer (SSL) certificate and/or a Transport Layer Security (TLS)certificate associated with a computer system of the entity; (viii) anumber and/or type of service of open ports of a computer networkassociated with the entity; (ix) an evaluation of security-relatedfields of an header section of HTTP response messages of hostsassociated with the entity; (x) a rate at which vulnerabilities arepatched in a computer network associated with the entity; (xi) anevaluation of file sharing traffic originating from a computer networkassociated with the entity; and/or (xii) a number of lost records and/orsensitivity of information in the lost records in a data breach of acomputer system associated with the entity.

The security class can be a security rating of the entity. The value ofthe security class parameter can be indicative of a security class aboveor below a target security rating. The statistical classifier can be:(i) a K-nearest neighbor algorithm, (ii) a support vector machine (SVM)model, or (iii) random forest classifier. Each entity data set caninclude two or more input parameters indicative of the security profileof the entity. The method can include, for a first input parameter ofthe two or more input parameters: determining a relationship between atleast one value of the first input parameter and at least one value of asecond input parameter; and storing the relationship in a database. Themethod can include determining relationships between a plurality ofvalues of the first input parameter and a plurality of values of thesecond input parameter. The plurality of values of the first inputparameter can include one or more values of the first input parameterand the plurality of values of the second input parameter can includeone or more values of the second input parameter.

The method can include receiving values of the two or more inputparameters for the particular entity; adjusting the value of the firstinput parameter of the two or more input parameters; determining thevalue of the second input parameter of the two or more input parametersbased on the stored relationship in the database; using the trainedstatistical classifier on the adjusted value of the first inputparameter and the determined value of the second input parameter toinfer a value of the security class parameter indicative of the securityclass of the particular entity; comparing the value of the securityclass parameter to a target value to determine whether the adjustment ofthe value of the first input parameter results in a value of thesecurity class parameter at, above, or below the target value. If theadjustment of the value of the first input parameter results in a valueabove the target value, the method can include generating a securityimprovement plan based on the adjusted value of the first inputparameter and the determined value of the second input parameter, suchthat, if executed by the particular entity, increases the value of thesecurity class parameter of the particular entity to or above the targetvalue.

The security improvement plan can include a target value for at leastone input parameter for the particular entity, in which the target valueis different than the value of the at least one input parameter. Themethod can include presenting the security improvement plan via a userinterface. The security improvement plan can include a prescription toadjust at least one of the input parameters. The method can includedetermining an explanation for the prescription using one or moreexplanation techniques selected from the group consisting of: (i) localinterpretable model-agnostic explanation (LIME), (ii) high-precisionmodel-agnostic explanation, (iii) Skater model interpretation, or (iv)random forest feature tweaking. The method can include presenting theexplanation via the user interface. The method can include, if theadjustment of the value of the first input parameter results in thevalue of the security class parameter being at or above the targetvalue, determining a target value for the first input parameter by:receiving two or more values of the first input parameter from two ormore entity data sets of entities having a value of the security classparameter greater than the target value; determining a mean of the twoor more values; generating a security improvement plan prescribing themean value for the first input parameter of the particular entity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a flowchart of an exemplary method for statistical modelingof entities of a particular type. FIG. 1B is a diagram of the workflowfor training the statistical model in FIG. 1A.

FIG. 2A is a flowchart of an exemplary method for using the trainedstatistical model. FIG. 2B is a flowchart of exemplary method forgenerating a security improvement plan. FIG. 2C is a diagram of theworkflow for generating the security improvement plan of FIG. 2B.

FIG. 3 is a diagram of an exemplary security improvement plan for aparticular entity.

FIG. 4 is a diagram of an exemplary computer system that may be used inimplementing the systems and methods described herein.

DETAILED DESCRIPTION

Disclosed herein are exemplary embodiments of systems and methods forgenerating security improvement plans for entities. The securityimprovement plans can include values for one or more modifiableparameters (also referred to as “input parameters” herein) that a givenentity can act upon to improve its security rating. Examples of securityratings and the determination of security ratings for entities can befound in commonly owned U.S. Pat. No. 9,973,524 issued May 15, 2018 andtitled “Information Technology Security Assessment System,” the entiretyof which is incorporated by reference herein. One example of a securityrating (provided by BitSight Technologies, Inc., Boston, Mass.) has ascale from 300 (lowest) to 900 (highest). In some embodiments, lowersecurity ratings signify high incidence of past security events and/orhigh risk of future security risk. Conversely, higher security ratingscan signify low incidence of past security events and/or low risk offuture security risk.

In the below disclosure, the following non-limiting example entity isutilized for illustrating the exemplary systems and methods describedherein:

-   -   Corporation A is a financial services company (static parameter        “industry”) having approximately 190 employees (static parameter        “size”) in the northwest region of the United States (static        parameter “location”).    -   Corporation A is seeking to increase its security rating from        its current security rating of 560 to the target security rating        of at least 720.

Similar Entities & Related Datasets

In some embodiments, the systems and methods described herein generateachievable security improvement plans by utilizing data associated withsimilar entities to the particular entity for which the plan isgenerated. In some embodiments, these similar entities can include thoseentities that have one or more static parameters that are shared withthe particular entity. In some embodiments, the similarity between afirst entity and a second entity is determined by a percentage of staticparameters. For example, the first entity may share at least 50% of thestatic parameters with the second entity and therefore be considered tobe “similar” to the second entity. In other examples, the first entityshares at least 70%, at least 80%, or at least 90% of the staticparameters with the second entity to be considered “similar” to thesecond entity.

The below table provides an example set of entities sharing one or morestatic parameters with the exemplary “Corporation A,” as describedabove.

TABLE 1 List of entities, their associated static parameters, andsimilarity to Corporation A. Static Parameters Entity {industry, size,location} Similarity Corporation {financial services, 200 ⅔ B employees,southwest US} Corporation {financial services, 150 3/3 F employees,northwest US} Corporation {financial services, 540 ⅔ K employees,northwest US} Corporation {financial services, 175 ⅔ Q employees,southeast US} Corporation {financial services, 50 ⅓ S employees,northeast US} Note, in this example, that the similarity of the size ofentities may be determined by a predetermined category of sizes forfinancial service companies (e.g., 1-50 employees, 51-200 employees,201-500 employees, 501 + employees).

In the above example utilizing a 50% threshold in determining similaritybetween entities, only Corporation S lacks in similarity to CorporationA in most of the static parameters. Thus, only Corporation B, F, K, andQ would be used to train the statistical model to generate a securityimprovement panel for Corporation A, as described below.

In some embodiments, similar entities having security ratings above andbelow the target security rating are selected for generating thesecurity improvement plan. Selecting entities having security ratingsabove the target security rating can contribute to determining whichvalues for modifiable parameters that is likely to increase theparticular entity's security rating. Selecting entities having securityratings below the target security rating can contribute to determiningwhich values will not lead the particular entity to its target securityrating (or above the target security rating). In some embodiments,similar entities having security ratings at, above, and below the targetsecurity rating are selected so that both of the above-describedbenefits are included in the generated security improvement plan. Insome embodiments, a minimum number (e.g., at least three, at least 5, atleast 10, etc.) of similar entities are selected for training thestatistical model. In some embodiments, the number of similar entitieshaving security ratings above the target security rating isapproximately equal to the number of similar entities having securityratings below the target security rating. For example, approximatelytwenty similar entities may be selected having security ratings abovethe target security rating and approximately twenty similar entities maybe selected below the security rating.

In some embodiments, for each of the similar entities, an entity dataset is obtained. Continuing the above example, Table 2 lists thesecurity ratings for the above ‘similar’ entities to Corporation A at aparticular time.

TABLE 2 List of similar entities and their corresponding securityratings at a particular time (e.g., 3 months ago, present date, etc.).Entity Security Rating Corporation B 710 Corporation F 535 Corporation K745 Corporation Q 680In some embodiments, the security ratings of the similar entitiesinclude security ratings over a period, e.g., from a first time (e.g., 3years ago, 1 year ago, 6 months ago, etc.) to a second time (e.g., 1year ago, 6 months ago, present date, etc.). In some embodiments, thesecurity ratings of entities may be averaged over some time period(e.g., within the last three months, last six months, last one year,etc.) to determine whether the entity should be selected. In someembodiments, other data related to security ratings can be obtained. Forexample, the other data can include data related to security events,components of the security ratings, analytics associated with thesecurity ratings, etc. Examples of data related to security ratings canbe found in U.S. patent application Ser. No. 16/360,641 titled “Systemsand methods for forecasting cybersecurity ratings based on event-ratescenarios,” having Attorney Docket No. BST-017.

Training the Statistical Model

In some exemplary methods discussed herein, to determine values for themodifiable input parameters for the security improvement plan of aparticular entity, a statistical model can be trained. In someembodiments, the statistical model can be trained on a plurality ofentity data sets of entities similar to the particular entity. Further,the plurality of entity data sets can be selected such that the similarentities have security ratings both above and below the target securityrating.

FIG. 1A is a flowchart illustrating a method 100 for statisticalmodeling of entities of a particular type. FIG. 1B is a diagramillustrating workflow 106 of training the statistical classifier 108.Step 102 of method 100 includes obtaining entity data including aplurality of entity datasets. Each entity data set 110 can be associatedwith a respective entity and include value(s) for one or more staticparameters 112 indicative of a type of the entity. For example, thestatic parameters 112 can include entity size, entity industry, and/orentity location. The values of the static parameters 112 for each entitydata set can indicate whether the type of the entity matches theparticular type associated with the particular entity (see discussionabove under heading “Similar Entities & Related Datasets”).

In some embodiments, each entity data set can include (i) values for oneor more input parameters 114 indicating the security profile of theentity and/or (ii) a value of a security class parameter 116 indicatingthe security class of the entity based on the value(s) of the inputparameter(s) 114. The security profile may include the securitypractices and/or security record of an entity. In some embodiments, theinput parameters 114 can include one or more of:

-   -   an amount of capital investment in security of the entity;    -   a measure of employee training in security of the entity;    -   a measure of organization of a team dedicated to information        security;    -   an amount of budget dedicated to information security;    -   a number and/or severity of botnet infection instances of a        computer system associated with the entity;    -   a number of spam propagation instances originating from a        computer network associated with the entity;    -   a number of malware servers associated with the entity;    -   a number of potentially exploited devices associated with the        entity;    -   a number of hosts authorized to send emails on behalf of each        domain associated with the entity;    -   a determination of whether a DomainKeys Identified Mail (DKIM)        record exists for each domain associated with the entity and/or        a key length of a public key associated with a Domain Name        System (DNS) record of each domain associated with the entity;    -   an evaluation of a Secure Sockets Layer (SSL) certificate and/or        a Transport Layer Security (TLS) certificate associated with a        computer system of the entity;    -   a number and/or type of service of open ports of a computer        network associated with the entity;    -   an evaluation of security-related fields of an header section of        HTTP response messages of hosts associated with the entity;    -   a rate at which vulnerabilities are patched in a computer        network associated with the entity;    -   an evaluation of file sharing traffic originating from a        computer network associated with the entity; or    -   a number of lost records and/or sensitivity of information in        the lost records in a data breach of a computer system        associated with the entity.

In some embodiments, an entity data set can include two or more inputparameters 114 (e.g., of those listed above). Thus, in some cases, theexemplary methods described herein can further include determining arelationship between a value of the first input parameter and a value ofthe second input parameter. This relationship can be stored in adatabase. For example, the number of botnet infections of an entity maybe correlated with the number of potentially exploited devicesassociated with the entity. This correlation can be stored andreferenced in the future. In some embodiments, the database includes therelationship between a plurality of values for the first input parameterand a plurality of values for the second input parameter. Relationshipsbetween values of the first and second parameters can be of a linear,non-linear, inverse, or other type. In some cases, the relationships canbe stochastic.

In some embodiments, the security class parameter 116 of an entity isassociated with, related to, or equal to the security rating of thatentity (e.g, on a scale from 300 to 900, as provided by BitSightTechnologies, Inc., Boston, Mass. and discussed above). For example, afirst value of the security class parameter 116 is associated with,related to, or equal to a first security rating (e.g., 600); a secondvalue of the security class parameter 116 is associated with, relatedto, or equal to a second security rating (e.g., 601); and so on. In someembodiments, the security class parameter 116 is associated with rangesof the security rating of the entity. For example, a first value of thesecurity class parameter 116 is associated with, related to, or equal toa first security rating range (e.g., 600-649); a second value of thesecurity class parameter 116 is associated with, related to, or equal toa second security rating (e.g., 650-659); and so on. In someembodiments, the value of the security class parameter 116 can indicatewhether the security rating of the entity is at, above, or below atarget security rating. For example, a first value of security classparameter is associated with, related to, or equal to a first set ofsecurity ratings at or above the target security rating (e.g., for atarget security rating of 720, the first set of security ratings is720-900); a second value of security class parameter is associated with,related to, or equal to a second set of security ratings below thetarget security rating (e.g., for a target security rating of 720, thesecond set of security ratings is 300-719).

In some embodiments, the method 100 can include selecting a target valuefor security class parameter 116 indicative of the security class forthe particular entity. Having selected a target value, the plurality ofentity data sets are chosen such that they include entity data set(s)for which the value of the security class parameter 116 is lower thanthe target value and entity data set(s) for which the value of thesecurity class parameter 116 is greater than the target value. Forexample, if the target value of the security class parameter 116 (e.g.,the security rating) for the particular entity is 720, then the datasets of one or more entities having a security rating less than 720 andthe data sets of one or more entities having a security rating greaterthan 720 are selected for training the statistical classifier 108. Insome cases, it can be beneficial to include entity data sets of havingsecurity class parameter values both above and below the target value inthe training of the statistical model so that the generated securityimprovement plan, as discussed further below, includes values for one ormore input parameters that can help the particular entity achieve thetarget security rating (or above the target security rating).Additionally or alternatively, the generated security improvement plancan provide values that can harm the particular entity's security rating(in other words, to illustrate for the particular entity ‘what not todo’ in their security practices).

Step 104 of method 100 includes training a statistical classifier toinfer a value of the security class parameter indicative of the securityclass for the particular entity based on values of one or more of theinput parameters indicative of a security profile of the particularentity. The training can include fitting the statistical classifier 108to the plurality of entity data sets 110. Examples of the statisticalclassifier 108 can include any suitable statistical model for this useand can include any one of the following algorithms or models: aK-nearest neighbor algorithm; a support vector machine (SVM) model; or adecision tree-based model. For example, the decision tree-based modelcan be a random decision forest classifier (also known as a ‘randomforest’). In some embodiments, the SVM model can include a radial basisfunction (RBF) kernel.

Generating Improvement Plans

FIG. 2A illustrates a method 200 for using the trained statisticalclassifier of method 100. In step 202 of method 200, values of inputparameter(s) for the particular entity are received. In step 204 ofmethod 200, the value of the first input parameter is adjusted (e.g.,increased or decreased). In step 206, the value of the second inputparameter is determined based on the stored relationship in thedatabase. Referring to the example provided above, if there is anincreased number of botnet infections (the value of the firstparameter), then there is an expected increase in the number ofpotentially exploited devices (the value of the second parameter) basedon the stored relationship. Therefore, the number of potentiallyexploited devices is determined to increase as well.

In step 206, the trained statistical classifier (see discussion aboveunder heading “Training the Statistical Model”) can be used on theadjusted value of the first input parameter and the determined value ofthe second input parameter to infer a value of the security classparameter of the entity. In step 208, the value of the security classparameter can be compared to a target value to determine whether theadjustment of the value of the first input parameter results in a valueof the security class parameter above or below the target value. Forexample, the classifier may infer a value of the security classparameter (e.g., the security rating) to be 685 based on an increasednumber of botnet infections. If the target value of the security classparameter (e.g., the security rating) is 720, then the adjustmentresults in a value of the security class parameter below the targetvalue (e.g., 685 compared to 720).

FIG. 2B illustrates a method 201 for generating a security improvementplan for the particular entity. FIG. 2C illustrates a workflow 211 forgenerating the security improvement plan using the trained classifier(refer to exemplary method 200 for description related to steps 202through 210). In step 212 of method 201, a security improvement plan 222can be generated for the particular entity. The security improvementplan can be based on the adjusted value of the first input parameter andthe determined value of the second input parameter. If executed by theparticular entity, the security improvement plan 222 can increase thevalue of the security class parameter of the particular entity to orabove the target value. In an ideal scenario, the particular entity isexpected to execute the generated security improvement plan 222 byattempting to attain each of the values of the modifiable inputparameters. In some embodiments, the security improvement plan 222 ispresented to a user (e.g., company representative, insurancerepresentative, etc.) via a user interface. FIG. 3 is a diagram of anexemplary security improvement plan 214 for the particular entity.

In some embodiments, exemplary method 200 and/or exemplary method 201can include determining the mean of two or more values of the firstinput parameter from two or more entity data sets of entities having avalue of the security class parameter greater than the target value.Methods 200 and/or 201 can include the generation of a securityimprovement plan 222 prescribing the mean value for the first inputparameter of the particular entity. In some embodiments, this techniquecan be repeated for each input parameter that is found to contribute toan improved security rating for the entity. For example, thecontribution of an input parameter to the security rating can bedetermine by steps 202-210 of methods 200 or 201.

In some embodiments, the security improvement plan for the particularentity can include a prescription to adjust at least one inputparameter. It can be beneficial to provide explanations to theparticular entity as to why modifying the values of which parametershelps the entity achieve the desired security rating. The method 201 caninclude one or more explanation techniques, e.g., local interpretablemodel-agnostic explanation (LIME), high-precision model-agnosticexplanation (referred to as ‘anchors’), Skater model interpretation,random forest feature tweaking, etc. In some embodiments, theexplanations can be presented to the user via the user interface.

Computer-Based Implementations

In some examples, some or all of the processing described above can becarried out on a personal computing device, on one or more centralizedcomputing devices, or via cloud-based processing by one or more servers.In some examples, some types of processing occur on one device and othertypes of processing occur on another device. In some examples, some orall of the data described above can be stored on a personal computingdevice, in data storage hosted on one or more centralized computingdevices, or via cloud-based storage. In some examples, some data arestored in one location and other data are stored in another location. Insome examples, quantum computing can be used. In some examples,functional programming languages can be used. In some examples,electrical memory, such as flash-based memory, can be used.

FIG. 4 is a block diagram of an example computer system 400 that may beused in implementing the technology described in this document.General-purpose computers, network appliances, mobile devices, or otherelectronic systems may also include at least portions of the system 400.The system 400 includes a processor 410, a memory 420, a storage device430, and an input/output device 440. Each of the components 410, 420,430, and 440 may be interconnected, for example, using a system bus 450.The processor 410 is capable of processing instructions for executionwithin the system 400. In some implementations, the processor 410 is asingle-threaded processor. In some implementations, the processor 410 isa multi-threaded processor. The processor 410 is capable of processinginstructions stored in the memory 420 or on the storage device 430.

The memory 420 stores information within the system 400. In someimplementations, the memory 420 is a non-transitory computer-readablemedium. In some implementations, the memory 420 is a volatile memoryunit. In some implementations, the memory 420 is a nonvolatile memoryunit.

The storage device 430 is capable of providing mass storage for thesystem 400. In some implementations, the storage device 430 is anon-transitory computer-readable medium. In various differentimplementations, the storage device 430 may include, for example, a harddisk device, an optical disk device, a solid-date drive, a flash drive,or some other large capacity storage device. For example, the storagedevice may store long-term data (e.g., database data, file system data,etc.). The input/output device 440 provides input/output operations forthe system 400. In some implementations, the input/output device 440 mayinclude one or more of a network interface devices, e.g., an Ethernetcard, a serial communication device, e.g., an RS-232 port, and/or awireless interface device, e.g., an 802.11 card, a 3G wireless modem, ora 4G wireless modem. In some implementations, the input/output devicemay include driver devices configured to receive input data and sendoutput data to other input/output devices, e.g., keyboard, printer anddisplay devices 460. In some examples, mobile computing devices, mobilecommunication devices, and other devices may be used.

In some implementations, at least a portion of the approaches describedabove may be realized by instructions that upon execution cause one ormore processing devices to carry out the processes and functionsdescribed above. Such instructions may include, for example, interpretedinstructions such as script instructions, or executable code, or otherinstructions stored in a non-transitory computer readable medium. Thestorage device 430 may be implemented in a distributed way over anetwork, such as a server farm or a set of widely distributed servers,or may be implemented in a single computing device.

Although an example processing system has been described in FIG. 4,embodiments of the subject matter, functional operations and processesdescribed in this specification can be implemented in other types ofdigital electronic circuitry, in tangibly-embodied computer software orfirmware, in computer hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter described inthis specification can be implemented as one or more computer programs,i.e., one or more modules of computer program instructions encoded on atangible nonvolatile program carrier for execution by, or to control theoperation of, data processing apparatus. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. The computer storage medium can be amachine-readable storage device, a machine-readable storage substrate, arandom or serial access memory device, or a combination of one or moreof them.

The term “system” may encompass all kinds of apparatus, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. A processingsystem may include special purpose logic circuitry, e.g., an FPGA (fieldprogrammable gate array) or an ASIC (application specific integratedcircuit). A processing system may include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program (which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code) can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astandalone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data (e.g., one ormore scripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Computers suitable for the execution of a computer program can include,by way of example, general or special purpose microprocessors or both,or any other kind of central processing unit. Generally, a centralprocessing unit will receive instructions and data from a read-onlymemory or a random access memory or both. A computer generally includesa central processing unit for performing or executing instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of nonvolatile memory, media andmemory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's user device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous. Other steps or stages may be provided,or steps or stages may be eliminated, from the described processes.Accordingly, other implementations are within the scope of the followingclaims.

Terminology

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting.

The term “approximately”, the phrase “approximately equal to”, and othersimilar phrases, as used in the specification and the claims (e.g., “Xhas a value of approximately Y” or “X is approximately equal to Y”),should be understood to mean that one value (X) is within apredetermined range of another value (Y). The predetermined range may beplus or minus 20%, 10%, 5%, 3%, 1%, 0.1%, or less than 0.1%, unlessotherwise indicated.

The indefinite articles “a” and “an,” as used in the specification andin the claims, unless clearly indicated to the contrary, should beunderstood to mean “at least one.” The phrase “and/or,” as used in thespecification and in the claims, should be understood to mean “either orboth” of the elements so conjoined, i.e., elements that areconjunctively present in some cases and disjunctively present in othercases. Multiple elements listed with “and/or” should be construed in thesame fashion, i.e., “one or more” of the elements so conjoined. Otherelements may optionally be present other than the elements specificallyidentified by the “and/or” clause, whether related or unrelated to thoseelements specifically identified. Thus, as a non-limiting example, areference to “A and/or B”, when used in conjunction with open-endedlanguage such as “comprising” can refer, in one embodiment, to A only(optionally including elements other than B); in another embodiment, toB only (optionally including elements other than A); in yet anotherembodiment, to both A and B (optionally including other elements); etc.

As used in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used shall only be interpreted as indicating exclusive alternatives(i.e. “one or the other but not both”) when preceded by terms ofexclusivity, such as “either,” “one of,” “only one of,” or “exactly oneof.” “Consisting essentially of,” when used in the claims, shall haveits ordinary meaning as used in the field of patent law.

As used in the specification and in the claims, the phrase “at leastone,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

The use of “including,” “comprising,” “having,” “containing,”“involving,” and variations thereof, is meant to encompass the itemslisted thereafter and additional items.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed. Ordinal termsare used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term), to distinguish the claim elements.

1-20. (canceled)
 21. A computer-implemented method for generating asecurity improvement plan for a particular organization, the methodcomprising: receiving an organization data set for the particularorganization, the organization data set comprising: (i) a value for atleast one static parameter indicative of a type of the organization;(ii) a value for at least two input parameters indicative of a securityprofile of the particular organization; and (iii) a value of a securityclass parameter indicative of a security class of the particularorganization; adjusting a value of a first input parameter of the inputparameters; determining a value of a second input parameter of the inputparameters based on a relationship between the value of the first inputparameter and the value of the second input parameter; using a trainedstatistical classifier on the adjusted value of the first inputparameter and the determined value of the second input parameter toinfer an adjusted value of the security class parameter; and if theadjustment of the value of the first input parameter results in anincreased value of the security class parameter, generating a securityimprovement plan based on the adjusted value of the first inputparameter and the determined value of the second input parameter, suchthat execution of the security improvement plan by the particularorganization results in an increase in the value of the security classparameter of the particular organization.
 22. The method of claim 21,further comprising: comparing the adjusted value of the security classparameter to a target value to determine whether the adjustment of thevalue of the first input parameter results in a value of the securityclass parameter at, above, or below the target value.
 23. The method ofclaim 22, wherein generating the security improvement plan comprises:generating the security improvement plan based on the adjusted value ofthe first input parameter and the determined value of the second inputparameter, such that execution of the security improvement plan by theparticular organization results in an increase in the value of thesecurity class parameter of the particular organization at or above thetarget value.
 24. The method of claim 21, wherein the securityimprovement plan comprises a target value or at least one inputparameter for the particular organization, the target value beingdifferent than the value of the at least one input parameter.
 25. Themethod of claim 21, further comprising: presenting the securityimprovement plan via a user interface.
 26. The method of claim 21,wherein the security improvement plan includes a prescription to adjustat least one of the input parameters, the method further comprising:determining an explanation for the prescription using one or moreexplanation techniques selected from the group consisting of: (i) localinterpretable model-agnostic explanation (LIME), (ii) high-precisionmodel-agnostic explanation, (iii) Skater model interpretation, or (iv)random forest feature tweaking.
 27. The method of claim 26, furthercomprising: presenting the explanation via the user interface.
 28. Themethod of claim 21, further comprising: determining a target value forthe first input parameter by: receiving two or more values of the firstinput parameter from two or more organization data sets of entitieshaving a value of the security class parameter greater than the targetvalue; and determining a mean of the two or more values, whereingenerating the security improvement plan comprises prescribing the meanvalue for the first input parameter of the particular organization. 29.The method of claim 21, wherein the relationship between at least onevalue of the first input parameter and at least one value of a secondinput parameter is stored in a database, and wherein the methodcomprises: retrieving the stored relationship from the database.
 30. Themethod of claim 21, wherein the static parameters comprise at least oneof (i) organization size, (ii) organization industry, and/or (iii)organization location.
 31. The method of claim 31, wherein the securityprofile comprises security practices and/or a security record of anorganization.
 32. The method of claim 1, wherein the one or more inputparameters indicative of the security profile of the organizationcomprise at least one of: an amount of capital investment in security ofthe organization; a measure of employee training in security of theorganization; a measure of organization of a team dedicated toinformation security; or an amount of budget dedicated to informationsecurity.
 33. The method of claim 1, wherein the one or more inputparameters indicative of the security profile of the organizationcomprise at least one of: a number and/or severity of botnet infectioninstances of a computer system associated with the organization; anumber of spam propagation instances originating from a computer networkassociated with the organization; a number of malware servers associatedwith the organization; a number of potentially exploited devicesassociated with the organization; a number of hosts authorized to sendemails on behalf of each domain associated with the organization; adetermination of whether a DomainKeys Identified Mail (DKIM) recordexists for each domain associated with the organization and/or a keylength of a public key associated with a Domain Name System (DNS) recordof each domain associated with the organization; an evaluation of aSecure Sockets Layer (SSL) certificate and/or a Transport Layer Security(TLS) certificate associated with a computer system of the organization;a number and/or type of service of open ports of a computer networkassociated with the organization; an evaluation of security-relatedfields of an header section of HTTP response messages of hostsassociated with the organization; a rate at which vulnerabilities arepatched in a computer network associated with the organization; anevaluation of file sharing traffic originating from a computer networkassociated with the organization; or a number of lost records and/orsensitivity of information in the lost records in a data breach of acomputer system associated with the organization.
 34. The method ofclaim 21, wherein the statistical classifier was trained by: obtainingorganization data including a plurality of organization data sets, eachorganization data set associated with a respective organization andincluding: (i) a value for at least one static parameter indicative of atype of the organization, wherein the values of the static parameterindicates that the type of the organization matches the particular type;(ii) a value for at least one input parameter indicative of a securityprofile of the organization; (iii) a value of a security class parameterindicative of a security class of the organization based on the value ofthe at least one input parameter; training the statistical classifier toinfer a value of the security class parameter indicative of the securityclass of a particular organization of the particular type based onvalues of the at least one input parameter indicative of a securityprofile of the particular organization, wherein training the statisticalclassifier comprises fitting the statistical classifier to the pluralityof organization data sets.
 35. The method of claim 36, wherein eachorganization data set includes two or more input parameters indicativeof the security profile of the organization, the method furthercomprising: for a first input parameter of the two or more inputparameters, determining the relationship between at least one value ofthe first input parameter and at least one value of a second inputparameter.
 36. The method of claim 35, further comprising: determiningrelationships between a plurality of values of the first input parameterand a plurality of values of the second input parameter, wherein theplurality of values of the first input parameter comprises the at leastone value of the first input parameter and the plurality of values ofthe second input parameter comprises the at least one value of thesecond input parameter.
 37. The method of claim 34, wherein the valuesof two or more static parameters for each of the organization data setsindicate that the type of the organization matches the particular type.38. The method of claim 34, further comprising: selecting a target valuefor the security class parameter indicative of the security class forthe particular organization, wherein the plurality of organization datasets includes at least one organization data set for which the value ofthe security class parameter is lower than the target value and at leastone organization data set for which the value of the security classparameter is at or above than the target value.
 39. The method of claim38, wherein the plurality of organization data sets includes at leastthree organization data sets for which the value of the security classparameter is lower than the target value and at least three organizationdata set for which the value of the security class parameter is at orabove than the target value.
 40. The method of claim 21, wherein thesecurity class is a security rating of the entity.